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Machine Learning · Practical ML

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Aurélien Géron · 3rd edition · 2022

💎 PremiumIntermediate★★★★★856 pagesISBN 978-1098125974

The most popular ML book worldwide. Covers classical ML and deep learning with practical code examples and real datasets.

Why you should read this
If you can only read ONE ML book, choose this one. It takes you from zero to deploying production ML models.

Key Topics

Linear RegressionDecision TreesRandom ForestsSVMNeural NetworksCNNsRNNsTransformersReinforcement LearningMLOps

Chapters (10)

1The ML Landscape12 key concepts

What is ML, types, challenges, workflow

2End-to-End ML Project16 key concepts

California housing price prediction pipeline

3Classification14 key concepts

Binary, multiclass, confusion matrix, ROC

4Training Models18 key concepts

Gradient descent, polynomial regression, regularization

5SVMs10 key concepts

Linear and kernel SVMs, margin maximization

6Decision Trees8 key concepts

CART, Gini, entropy, pruning

7Ensemble Methods14 key concepts

Random forests, boosting, bagging, stacking

8Dimensionality Reduction10 key concepts

PCA, t-SNE, UMAP, LLE

9Introduction to Neural Networks16 key concepts

Perceptrons, backpropagation, Keras API

10Deep Computer Vision (CNNs)18 key concepts

Convolutional layers, transfer learning, object detection

Real-World Applications
  • House price prediction
  • Image classification
  • Sentiment analysis
  • Recommendation systems
Best For
ML engineersData scientistsSoftware developersStudents

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